Neonatal digital twin based healthcare solution

Abstract

A method of operation of an electronic device is disclosed. The method of operation includes the steps of: generating a digital twin to mimic the growth stage of a subject based on the subject's genetic data, biological data, and environmental data; monitoring the growth of the digital twin and, when the growth stage of the digital twin matches a preset state, obtaining guide information that matches the preset state; and providing the guide information to a terminal of the subject's guardian.

Description

Technology Field

[0001] The present disclosure relates to the operation of an electronic device that performs digital twin-based health management, and more specifically, to a solution that utilizes artificial intelligence technology to predict the growth and development process of a newborn, periodically provides customized health information accordingly, and helps the child's growth through interaction with parents. Background Technology

[0002] Recently, Digital Twin technology has been utilized in innovative ways across various industries and is attracting particular attention as a technology that enables the provision of personalized services reflecting the individual characteristics and needs of users. By replicating real-world objects or systems in a virtual space, Digital Twins allow for more precise and efficient decision-making through simulations and predictive analytics. This technology is being actively applied not only in traditional industries such as manufacturing, aviation, urban planning, and energy, but also in the healthcare and bio sectors.

[0003] In the medical field, personalized digital twins are being generated based on various health data, such as individual biometric information, genomic data, lifestyle habits, and medical history. These are being utilized in diverse ways, including early disease prediction, treatment route recommendations, health status monitoring, and the provision of customized health management solutions. For instance, research is underway to predict the risk of cardiovascular disease in adults or simulate blood sugar changes in patients with chronic diseases, and some of these technologies are even evolving into commercial services. This approach significantly aids in precisely assessing a patient's condition and taking proactive preventive measures, contributing to the realization of individualized medicine that moves beyond conventional, standardized treatment methods.

[0004] However, most digital twin-based healthcare technologies developed to date have focused on the adult population and are often designed to address specific diseases (e.g., cancer, diabetes, heart disease). Consequently, there is a near absence of personalized digital twin-based solutions capable of monitoring and managing the overall health status of newborns in the early stages of life, particularly immediately after birth. Newborns require more meticulous and continuous health care because their physiological characteristics and developmental rates differ significantly from adults, and they possess relatively lower resistance to disease. Nevertheless, the current market lacks digital twin technology for newborns, creating an urgent need for the development of scientific and practical tools that can be utilized in clinical settings. Prior art literature

[0005] Published Patent Application No. 10-2024-0160084 The problem to be solved

[0006] The present disclosure provides a method of operation for an electronic device that collects genetic information, biological information, environmental information, etc., of a subject, such as a newborn, to generate a human digital twin, and predicts the growth and development process based thereon to provide personalized health management advice.

[0007] The purposes of the present disclosure are not limited to those mentioned above, and other purposes and advantages of the present disclosure not mentioned may be understood from the following description and will be more clearly understood from the embodiments of the present disclosure. Furthermore, it will be readily apparent that the purposes and advantages of the present disclosure can be realized by the means and combinations thereof set forth in the claims. means of solving the problem

[0008] A method of operation of an electronic device according to one embodiment of the present disclosure comprises: generating a digital twin to mimic the growth stage of a subject based on the subject's genetic data, biological data, and environmental data; monitoring the growth of the digital twin to identify whether the growth state of the digital twin matches a preset stage or a preset state; and, if the growth state of the digital twin matches the preset state or a preset stage, providing guide information to a terminal of the subject's guardian.

[0009] The method of operation of the electronic device may include the step of training a prediction model to predict changes in the digital twin based on the subject's biometric data and environmental data input through the guardian's terminal at multiple points in time, and the step of modeling the growth and development of the digital twin according to the age of the subject based on the sequential prediction results of the prediction model.

[0010] In this case, the step of providing the guide information may include: providing at least one query matching the preset step or the preset state to the guardian's terminal; when a response to the query is received from the guardian's terminal, identifying the subject's growth status information according to the response; inputting the growth status information into the prediction model to obtain growth prediction information of the digital twin; and providing detailed guide information including at least one of a customized diet, medication guidance, exercise prescription, and growth prescription based on the growth prediction information.

[0011] The step of providing the detailed guide information above may provide detailed guide information including at least one of a customized diet, medication guidance, exercise prescription, and growth prescription by comparing the growth goal set through the guardian's terminal with the growth prediction information through a generative model.

[0012] In this case, the step of providing the detailed guide information may involve selecting at least one guide item among a customized diet, medication guidance, exercise prescription, and growth prescription based on the output of the generative model, selecting at least one product suitable for the subject and the guardian through the generative model from a product list containing multiple products related to the selected guide item, and providing information about the selected product to the guardian's terminal. Effects of the invention

[0013] The method of operation of an electronic device according to the present disclosure not only helps to prevent diseases early and promote healthy growth by continuously monitoring and predicting the health status of a subject (e.g., a newborn), but also enables more accurate identification of the baby's developmental status through interaction with parents and provides customized information accordingly, thereby increasing childcare efficiency.

[0014] In particular, the method of operation of an electronic device according to the present disclosure has the advantage of enabling continuous indirect growth monitoring even when interaction with a guardian is not performed by tracking the continuous growth process through a digital twin, and can improve the accuracy of the digital twin and provide high-quality guidance information through communication with the guardian performed at each growth stage.

[0015] The method of operation of an electronic device according to the present disclosure is applicable to growth management of infants and adolescents as well as newborns, customized intervention for children with developmental delays, or health monitoring of children with chronic diseases. Brief explanation of the drawing

[0016] FIG. 1 is a diagram illustrating the operation of an electronic device according to one embodiment of the present disclosure to construct a digital twin of a subject and to communicate with a guardian's terminal to update the digital twin. FIG. 2 is a block diagram for explaining the configuration of an electronic device according to one embodiment of the present disclosure, FIG. 3 is a drawing for sequentially explaining the operation of individual modules within an electronic device according to one embodiment of the present disclosure, and FIG. 4 is an algorithm for explaining a series of processes in which an electronic device according to one embodiment of the present disclosure interacts with a guardian's terminal to provide guide information. Specific details for implementing the invention

[0017] Before specifically describing the present disclosure, the method of description in the specification and drawings is described.

[0018] First, the terms used in this specification and claims have been selected based on general terms considering their functions in the various embodiments of this disclosure. However, these terms may vary depending on the intent of those skilled in the art, legal or technical interpretations, and the emergence of new technologies. Additionally, some terms have been arbitrarily selected by the applicant. Such terms may be interpreted according to the meanings defined in this specification; in the absence of specific definitions, they may be interpreted based on the overall content of this specification and common technical knowledge in the relevant field.

[0019] In addition, the same reference numbers or symbols described in each drawing attached to this specification represent parts or components that perform substantially the same function. For convenience of explanation and understanding, the same reference numbers or symbols are used to describe different embodiments. That is, even if components having the same reference number are all depicted in multiple drawings, the multiple drawings do not imply a single embodiment.

[0020] Additionally, in this specification and claims, terms including ordinal numbers, such as "first," "second," etc., may be used to distinguish between components. These ordinal numbers are used to distinguish identical or similar components from one another, and the meaning of the terms should not be limited by the use of such ordinal numbers. For example, the order of use or arrangement of components combined with such ordinal numbers should not be restricted by the number. If necessary, each ordinal number may be used interchangeably.

[0021] In this specification, singular expressions include plural expressions unless the context clearly indicates otherwise. In this application, terms such as "comprising" or "consisting of" are intended to specify the existence of the features, numbers, steps, actions, components, parts, or combinations thereof described in the specification, and should be understood as not precluding the existence or addition of one or more other features, numbers, steps, actions, components, parts, or combinations thereof.

[0022] In the embodiments of the present disclosure, terms such as "module," "unit," "part," etc. are used to refer to a component that performs at least one function or operation, and such component may be implemented in hardware or software, or in a combination of hardware and software. Additionally, a plurality of "modules," "units," "parts," etc. may be integrated into at least one module or chip and implemented as at least one processor, except where each needs to be implemented in specific individual hardware.

[0023] Furthermore, in the embodiments of the present disclosure, when a part is described as being connected to another part, this includes not only a direct connection but also an indirect connection through another medium. Additionally, the meaning that a part includes a certain component implies that, unless specifically stated otherwise, it does not exclude other components but may include additional components.

[0024] FIG. 1 is a diagram illustrating the operation of an electronic device according to one embodiment of the present disclosure to build a digital twin of a subject and to communicate with a guardian's terminal to update the digital twin.

[0025] Referring to FIG. 1, the electronic device (100) can create and manage a digital twin (10) of a subject (1).

[0026] The subject (1) may be a newborn, infant, etc., but may also be a minor, adult, etc.

[0027] The digital twin (10) is constructed based on the subject's (1) genetic data, biological data, and environmental data, and corresponds to a digital entity that grows by mimicking the subject's (1) growth stages. The digital twin (10) can grow by mimicking the subject's (1) entire lifespan and can be used as a tool to predict the subject's (1) future health or growth.

[0028] The electronic device (100) can communicate with the terminal (200) of the guardian (2) of the subject (1). The guardian (2) may be the subject's (1) parents, dedicated care personnel, dedicated medical personnel, attending physician, etc., but is not limited thereto. The terminal (200) may be various terminal devices such as smartphones, tablet PCs, wearable devices, desktop PCs, etc.

[0029] Specifically, the electronic device (100) can provide interaction services through the guardian's (2) terminal (200) according to the growth stage. For example, a messenger or chat service may be provided through the generative model (e.g., LLM (Large Language Model)) of the electronic device (100), and at this time, information regarding the growth status of the subject (1) may be obtained according to the response of the guardian (2) input through the terminal (200).

[0030] In this case, the electronic device (100) not only updates the digital twin (10) based on information about the growth status entered by the guardian (2), but also predicts future growth in advance through the digital twin (10) and provides guidance information related to the growth of the subject (1) to the guardian's (2) terminal (200). The guidance information may include, but is not limited to, a customized diet, medication guidance, exercise prescription, growth prescription, etc.

[0031] Thus, the electronic device (100) communicates with the guardian (2) according to the growth stage and provides necessary guide information for each growth stage, and also continuously manages the quality of growth prediction or guidance by continuously updating the digital twin (10) according to the growth status of the subject (1) confirmed by the guardian (2) for each growth stage.

[0032] This will be explained in more detail through the drawings below.

[0033] FIG. 2 is a block diagram for explaining the configuration of an electronic device according to one embodiment of the present disclosure.

[0034] Referring to FIG. 2, the electronic device (100) may include a memory (110), a communication interface (120), a processor (130), etc. The electronic device (100) may be implemented as a service server that communicates with a guardian's terminal matched to each of a plurality of subjects to provide services (such as predicting the subject's growth, diagnosing the growth status, and providing guidance), and may be implemented as a database management server, an AI server, etc. composed of at least one computer, but is not limited thereto.

[0035] The memory (110) is configured to store at least one instruction or data related to an operating system (OS) for controlling the overall operation of the components of the electronic device (100) and the components of the electronic device (100).

[0036] The memory (110) may include non-volatile memory such as ROM or flash memory, and may include volatile memory such as DRAM. Additionally, the memory may include auxiliary storage devices such as a hard disk or SSD (Solid State Drive).

[0037] Referring to FIG. 2, the memory (110) may include a digital twin (10) that matches at least one subject, a prediction model (111), a generative model (112), etc.

[0038] The digital twin (10) may be constructed based on the subject's (1) genetic data, biological data, and environmental data. Specifically, the digital twin (10) may be configured based on immutable genetic data, variable genetic data, biological data, and environmental data, and the variable items may be updated according to the prediction of the prediction model (111) described later.

[0039] Genetic data may include information regarding the genome, epigenome, transcriptome, proteome, metabolome, microbiome, etc. The electronic device (100) can collect genetic data of the subject (1) from a server such as a hospital.

[0040] In addition to growth-related items such as height, weight, and specific gravity of body components, biometric data may include blood sugar, heart rate, electrocardiogram, oxygen saturation, body temperature, sleep patterns, and stress levels, and may be measured for a certain period of time (or a certain number of times) by a medical institution such as a hospital or by a wearable device worn by the subject (1). Furthermore, biometric data may include data such as feeding amount, number of bowel movements, crying pattern (frequency, duration), and movement during sleep, which are input by the guardian's terminal (200).

[0041] Environmental data may include air quality, ultraviolet rays, and noise in the area (outdoor) where the subject (1) lives, as well as temperature and humidity in the space (indoor) where the subject (1) lives, and may be measured for a certain period of time (a certain number of times) or longer through at least one sensor device.

[0042] The electronic device (100) can generate a digital twin (10), which is a digital entity for mimicking the growth of the subject (1), by collecting the aforementioned genetic data, biological data, environmental data, etc. from the guardian's (2) terminal (200), wearable device, sensor device, hospital server, etc.

[0043] The prediction model (111) is a model for predicting the growth and health of the digital twin (10).

[0044] The prediction model (111) can be customized to predict changes in the digital twin based on the biometric data and environmental data of the subject (1) input through the guardian's (2) terminal (200) at multiple points in time. That is, with a basic model provided for predicting the pattern of change in the digital twin at sequential future points in time based on the digital twin composed of genetic data, biometric data, and environmental data, the basic model is individually fine-tuned based on the biometric / environmental data obtained for each individual subject, and as a result, multiple prediction models can be provided that are matched to each subject.

[0045] The prediction model (111) can predict the values ​​of each item of the biometric data constituting the digital twin (10) for at least one future point in time based on its own data constituting the digital twin (10), and can also perform the prediction by additionally reflecting the actual growth status of the subject (1) confirmed by each growth stage.

[0046] At this time, the prediction result generated by the prediction model (111) can be applied to the digital twin (10) that grows in accordance with the age of the subject (1) over time to model the growth of the digital twin (10), and can also be used as an indicator to predict the future growth state of the digital twin (10) that has not yet been applied.

[0047] That is, based on the repeating modeling cycle, the result predicted by the prediction model (111) for the point in time of the next cycle is applied as modeling or an update to the digital twin (10) as the point in time of the next cycle described above arrives, so that modeling based on the short-term prediction result for the next cycle can be performed for each modeling cycle. On the other hand, a prediction for a future point in time corresponding to a time further than the modeling cycle based on the current point in time is also performed, and although such a prediction is not applied to the immediate modeling, it is a concept that can be provided to the guardian's terminal (200), etc., as a prediction result for the digital twin (111). That is, the prediction model (111) can perform not only short-term predictions corresponding to the modeling cycle but also non-period long-term predictions.

[0048] The prediction model (111) can be implemented as a time-series-based prediction model such as a Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Temporal Convolutional Networks (TCN), a Transformer-based model, a Variational Autoencoder (VAE), or a multimodal model that predicts by integrating the outputs of different types of models, and is not limited thereto.

[0049] The generative model (112) is a model for providing guidance information related to the growth of the subject (1), and can be implemented as a transformer-based LLM (Large Language Model), sLLM, etc., but is not limited thereto. The generative model (112) can provide text that composes guidance information based on the subject's (1) current growth status as well as the future growth prediction results predicted by the prediction model (111).

[0050] The guide information may include information on at least one guide item among a customized diet, medication guidance, exercise prescription, and growth prescription. To this end, the generative model (112) may be trained based on various web data containing nutritional standards, guidelines for growth and development stages, and the latest medical information.

[0051] To this end, the generative model (112) can be trained based on text information regarding various products (including services) or instructions corresponding to individual guide items. The products or instructions may include, for example, toys, teaching materials, methods of play, food, supplies, programs (exercise, meditation, learning, etc.), but are not limited thereto.

[0052] In this regard, the generative model (112) may be trained on the ingredients / program (composition) of various products, consumer reviews, test reports / certificates, and may recommend at least one product / service that can be helpful to the subject (1).

[0053] The communication interface (120) can be connected to an external server and / or terminal device through one or more networks, and can exchange data through various wired and wireless communication methods.

[0054] Wireless communication may include at least one of the following communication methods: LTE (long-term evolution), LTE-A (LTE Advance), 5G (5th Generation) mobile communication, CDMA (code division multiple access), WCDMA (wideband CDMA), UMTS (universal mobile telecommunications system), WiBro (Wireless Broadband), GSM (Global System for Mobile Communications), DMA (Time Division Multiple Access), WiFi (Wi-Fi), WiFi Direct, Bluetooth, NFC (near field communication), Zigbee, etc.

[0055] Wired communication may include at least one of communication methods such as Ethernet, optical network, USB (Universal Serial Bus), Thunderbolt, and HDMI (High Definition Multimedia Interface).

[0056] Meanwhile, communication methods are not limited to the examples described above and may include new communication methods that emerge with technological advancements.

[0057] The electronic device (100) can periodically monitor the growth status of the subject at each growth stage by communicating with the guardian's terminal of various subjects through the communication interface (120).

[0058] Meanwhile, unlike FIG. 2, at least one of the digital twin (10), prediction model (111), and generative model (112) may be stored in a separate database or a separate AI server rather than in the electronic device (100). In this case, the electronic device (100) may communicate with the aforementioned separate database or AI server through a communication interface (120).

[0059] The processor (130) is connected to the memory (110) and can control the electronic device (100) by executing at least one instruction stored in the memory (110).

[0060] To this end, the processor (130) may be implemented as a general-purpose processor such as a CPU (Central Processing Unit) or AP (Application Processor), a graphics-dedicated processor such as a GPU (Graphic Processing Unit) or VPU (Vision Processing Unit), or an artificial intelligence-dedicated processor such as an NPU (Neural Processing Unit). The processor may include volatile memory such as SRAM.

[0061] When a method according to one embodiment of the present disclosure includes a plurality of operations, the plurality of operations may be performed by a single processor or by a plurality of processors included in an electronic device (100). For example, when a first operation, a second operation, and a third operation are performed by a method according to one embodiment, the first operation, the second operation, and the third operation may all be performed by a first processor, or the first operation and the second operation may be performed by a first processor (e.g., a general-purpose processor) and the third operation may be performed by a second processor (e.g., an artificial intelligence dedicated processor).

[0062] One or more processors may be implemented as a single-core processor comprising one core, or as one or more multicore processors comprising multiple cores (e.g., homogeneous multicore or heterogeneous multicore). When one or more processors are implemented as multicore processors, each of the multiple cores included in the multicore processor may include internal processor memory such as on-chip memory, and a common cache shared by multiple cores may be included in the multicore processor. Additionally, each of the multiple cores included in the multicore processor (or some of the multiple cores) may independently read and execute program instructions for implementing a method according to one embodiment of the present disclosure, or all (or some) of the multiple cores may be linked together to read and execute program instructions for implementing a method according to one embodiment of the present disclosure.

[0063] When a method according to one embodiment of the present disclosure includes a plurality of operations, the plurality of operations may be performed by one of the plurality of cores included in a multi-core processor, or may be performed by a plurality of cores. For example, when a first operation, a second operation, and a third operation are performed by a method according to one embodiment, the first operation, the second operation, and the third operation may all be performed by a first core included in a multi-core processor, or the first operation and the second operation may be performed by a first core included in a multi-core processor and the third operation may be performed by a second core included in a multi-core processor.

[0064] In the embodiments of the present disclosure, a processor may mean a system-on-chip (SoC) in which one or more processors and other electronic components are integrated, a single-core processor, a multi-core processor, or a core included in a single-core processor or a multi-core processor, wherein the core may be implemented as a CPU, GPU, APU, MIC, DSP, NPU, hardware accelerator, or machine learning accelerator, but the embodiments of the present disclosure are not limited thereto.

[0065] Referring to FIG. 2, the processor (120) can control a digital twin construction module (131), an interaction module (132), a growth prediction module (133), a guide provision module (134), etc. Each of these modules may correspond to a functional unit module implemented in software and / or hardware.

[0066] The digital twin construction module (131) can create a digital twin (10), which is a digital entity for mimicking the growth of the subject (1), by collecting the aforementioned genetic data, biological data, environmental data, etc. from the guardian's (2) terminal (200), wearable device, sensor device, hospital server, etc.

[0067] However, there may be cases where at least one item constituting the biometric data or environmental data is omitted during a certain period of the entire period (e.g., failure / defect of the measuring device, communication failure, etc.). In this case, the digital twin construction module (131) can perform estimation through linear interpolation or an artificial intelligence model for interpolation (e.g., RNN) based on data collected during each adjacent period corresponding to before and after the omitted period.

[0068] The digital twin construction module (131) can statistically define the correlation between each item constituting the genetic data and each item constituting the biological data based on the genetic data and biological data of various subjects, and can generate a digital twin (10) including the correlation between the genetic data and the biological data according to this correlation.

[0069] The interaction module (132) is a module for performing interaction with the guardian (2) through the guardian (2)'s terminal (200). The interaction module (132) can provide a query generated through the generative model (112) and receive a response input by the guardian (2) from the terminal (200). Specifically, the interaction module (132) can obtain information about the growth status of the subject (1) by periodically conversing with the guardian (2) according to the subject (1)'s growth stage according to a certain period or certain condition (e.g., elapsed time, height or weight reaching a certain value, etc.). The growth stage may be a concept defined to advance to the next stage according to a certain period, or a concept defined to move to the next stage whenever a certain condition is satisfied, but is not limited thereto. For example, growth stages may be defined by age, such as the neonatal period (4 weeks old), infancy (1 to 12 months), and toddlerhood (1 to 3 years), or by transitioning to the next stage when specific conditions are met, such as an increase in height / weight or the achievement of developmental milestones.

[0070] The growth prediction module (133) can predict the growth of the digital twin (10) through the prediction model (111). Specifically, the growth prediction module (133) can sequentially predict biological data for at least one future point in time by inputting past data (genetic data, biological data, environmental data) including the current point in time of the digital twin (10) into the prediction model (111). At this time, the growth prediction module (133) can further increase the accuracy of the prediction of the growth of the digital twin (10) by additionally inputting information regarding the growth status of the subject (1) based on the response of the guardian (2) confirmed by each growth stage into the prediction model (111).

[0071] The growth prediction module (133) can model the digital twin (10) according to the age of the subject (1) as the digital twin (10) grows in accordance with the predicted growth prediction results. That is, the biometric data constituting the digital twin (10) can be modeled sequentially according to the growth prediction results described above and in accordance with the rate of increase in the age of the subject (1). At this time, the growth or update of the digital twin (10) may be performed at a shorter interval than the interval for receiving the response from the guardian (2), thereby allowing the estimation of the actual growth status of the subject (1) in real time to be performed via the digital twin (10). This modeling process can be performed at regular intervals so that the update of the digital twin (10) can be performed.

[0072] The guide provision module (134) is a module for providing guide information that helps the growth of the subject (1), and can generate guide information through the generative model (112) described above.

[0073] Specifically, the guide providing module (134) monitors the growth process of the continuously growing digital twin (10), and when the digital twin (10) matches a preset state or a preset stage (e.g., growth stage), it can provide guide information to the guardian's (2) terminal (200).

[0074] The preset state may correspond to, for example, a state in which height / weight, etc. is below a certain percentage compared to the average of other subjects of the same age, a state in which a disease or disorder is identified based on the value of the biometric data, or a state in which at least one of the items of the biometric data reaches a preset standard value, but is not limited thereto.

[0075] The pre-set stages may correspond to individual growth stages, and may be identified as matching the pre-set stage whenever sequentially reaching, for example, the neonatal period (4 weeks after birth), infancy (1 month to 12 months), toddlerhood (1 to 3 years), early childhood (3 to 6 years), middle childhood (6 to 12 years), etc., but are not limited thereto.

[0076] For example, the guide providing module (134) can generate and provide at least one guide item that can help to change the predicted future growth prediction results more positively without adversely affecting the current health condition of the subject (1) through the generative model (112).

[0077] FIG. 3 is a drawing for sequentially explaining the operation of individual modules within an electronic device according to one embodiment of the present disclosure.

[0078] Referring to FIG. 3, the digital twin construction module (131) can construct a digital twin (10) of a subject (1) including genetic data, biological data, and environmental data of the subject (1) (S310).

[0079] At this time, the growth prediction module (133) performs growth prediction by continuously predicting the pattern of change in the biometric data of the digital twin (10) according to the modeling cycle through the prediction model (111), while also continuously updating the digital twin (10) that grows according to the age of the subject (1) by sequentially modeling it according to the prediction results (S320).

[0080] In this way, during the growth process, when the digital twin (1) matches a preset state or preset stage, the interaction module (132) can perform interaction with the guardian (2) through the terminal (200) (S330). For example, a question-and-answer session may be conducted according to the growth stage, and the specific process will be described in more detail later through FIG. 4.

[0081] When a growth status (e.g., height / weight, developmental status, emotional developmental status, whether there is an abnormality in biometric data / degree of abnormality, etc.) is confirmed according to the response of the guardian (2), the interaction module (132) can transmit information about the confirmed growth status to the growth prediction module (133) (S340).

[0082] At this time, the growth prediction module (133) can input the growth status of the subject (1) currently being identified into the prediction model (111) and apply it to the growth prediction of the digital twin (10), which contributes to increasing the accuracy of the growth prediction or modeling.

[0083] Meanwhile, the guide providing module (134) can also monitor the growth process of the digital twin (10) and, in conjunction with the interaction module (132), provide guide information that helps the growth of the subject (1) (S350). Specifically, the guide providing module (134) can generate guide information that helps the growth of the subject (1) based on the current growth stage, the growth status confirmed by the interaction module (132), etc., and the generated guide information can be provided to the guardian (2)'s terminal (200) by the interaction module (132).

[0084] Meanwhile, FIG. 4 is an algorithm for explaining a series of processes in which an electronic device according to one embodiment of the present disclosure interacts with a guardian's terminal to provide guide information.

[0085] Referring to FIG. 4, the guide providing module (134) can provide at least one query matching a preset stage or preset state reached by the digital twin (10) to the guardian's (2) terminal through the interaction module (132) (S410). For example, when the toddler stage begins, the guide providing module (134) can generate queries such as “What is the child’s height and weight?” and “Does the child walk well?” through the generative model (112).

[0086] And, when a response to the above query is received from the terminal (200) of the guardian (2), the guide providing module (134) can identify the growth status information of the subject according to the response (S420 - Y). However, if the growth status information required according to the query is not sufficiently identified (S420 - N), the electronic device (100) may secure sufficient growth status information by providing an additional query generated by the generative model (112) (again, S410).

[0087] At this time, the growth prediction module (133) can input growth status information into the prediction model (111) to obtain growth prediction information of the digital twin (10) (S430). The growth prediction information may be for at least one point in the future.

[0088] And, the guide providing module (134) can provide detailed guide information including at least one of a customized diet, medication guidance, exercise prescription, and growth prescription based on growth prediction information (S440). At this time, the guide providing module (134) can provide various guide information for physical and emotional development suitable for the growth stage of the subject (1) estimated by the digital twin (10).

[0089] In addition, as an example, the guide providing module (134) may provide detailed guide information including at least one of a customized diet, medication guidance, exercise prescription, and growth prescription by comparing the growth goal set by the guardian's (2) terminal (200) through the generative model (112) with the growth prediction information described above.

[0090] Specifically, if the difference between the growth prediction information and the growth goal exceeds a preset threshold for the target item (e.g., height, weight, etc.), the guide providing module (134) may provide detailed guide information including a customized diet, medication guidance, exercise prescription, or growth prescription to reduce the difference.

[0091] For example, if the current age corresponds to 1 year of birth and the target height at 1 year and 6 months is 85 cm, while the predicted height at that time according to growth prediction information is 83 cm, the guide providing module (134) can provide detailed guide information, such as a customized diet or growth prescription, to accelerate growth in the current state. At this time, the guide providing module (134) can provide detailed guide information that is suitable for the subject (1) corresponding to 1 year of birth and helpful for growth through a generative model (112). For example, information on a customized diet / exercise program that the subject (1) corresponding to 1 year of birth can sufficiently perform can be provided.

[0092] Additionally, the guide providing module (134) can select at least one guide item among a customized diet, medication guidance, exercise prescription, and growth prescription according to the output of the generative model (112), and can select at least one product suitable for the subject (1) and guardian (2) through the generative model (112) from a product list containing multiple preset products related to the selected guide item.

[0093] The multiple pre-configured products (including services) may be products from product sellers registered in the database of the electronic device (100) by contractual relationship, or various products identified by searching on the web. The guide provision module (134) may provide customized educational information to help the intellectual, emotional, and physical development of the baby. For example, this may include toys or teaching materials suitable for the baby's cognitive development stage, play methods that help emotional development, and food or supplies most suitable for the baby's growth stage and health condition, but is not limited thereto.

[0094] Specifically, the guide providing module (134) can select at least one suitable product by inputting at least one of the subject's growth stage, health status, and guardian's preference into the generative model (112) along with a plurality of preset products (including services).

[0095] Additionally, the guide providing module (134) can select a product that is expected to have a positive effect based on past interactions by receiving information about the past usage history of the subject (1) for at least one product from the guardian's terminal and inputting it into the generative model (112).

[0096] And, the guide providing module (134) can provide information about the selected product to the guardian's (2) terminal (200) through the interaction module (132).

[0097] As a result, the method of operation of the electronic device (100) according to the present disclosure has the effect of recommending products necessary for the baby in a timely manner, thereby increasing the convenience of parents and helping the healthy growth of the child.

[0098] Meanwhile, as described above, the electronic device (100) receives growth status information from the guardian and / or provides guidance information when the digital twin reaches a preset state or preset stage according to the periodic modeling of the digital twin.

[0099] In this regard, the electronic device (100) may predict the target time when the digital twin reaches a preset state or preset stage through a prediction model (111).

[0100] Specifically, based on the current first point in time, a second point in time, which is the next modeling point in time, and a third point in time, which is the modeling point in time following the second point in time, can be identified. At this time, the electronic device (100) predicts the digital twin of the second point in time (e.g., biometric data, etc.) based on the digital twin of the current first point in time through a prediction model (111), and when the second point in time arrives, it can model the digital twin according to the predicted result.

[0101] At this time, the electronic device (100) may perform additional predictions for a third time point based on the digital twin predicted for the second time point before the second time point arrives. That is, for each modeling cycle, predictions may not be made for only one step cycle according to the modeling cycle, but sequential predictions may be performed for two or more steps.

[0102] Here, based on the prediction result, the electronic device (100) can identify whether the digital twin reaches a preset state or stage before the third time point arrives. Specifically, if it is predicted that it will grow further beyond the preset state or preset stage at the third time point, the electronic device (100) can predict the target time point (between the second time point and the third time point) at which the digital twin reaches the preset state or preset stage based on the difference in growth between the digital twin predicted for the second time point and the digital twin predicted for the next third time point.

[0103] In this case, the time at which the electronic device (100) requests growth status information from the guardian's terminal (200) and provides guidance information accordingly may vary depending on the time interval between the second time point and the target time point.

[0104] If the corresponding time interval is greater than a certain value, the electronic device (100) may proceed with the process of requesting growth status information from the guardian's terminal (200) at the target time and providing guidance information. In this case, the electronic device (100) may continue sequential periodic modeling by newly applying the modeling cycle based on the target time rather than the predicted third cycle. That is, a change in the reference time occurs in which the modeling cycle (interval) remains the same but the reference time changes.

[0105] On the other hand, if the corresponding time interval is less than a certain value, the electronic device (100) may proceed with the process of requesting growth status information from the guardian's terminal (200) at a second time point and providing guidance information. In this case, the time point at which the modeling cycle is applied remains the same, and the next modeling can be performed in the third cycle without change.

[0106] As a result, not only is an inefficient situation prevented where the prediction result changes immediately based on growth status information confirmed by the guardian right after the modeling process at the second time point is performed, but an inefficient situation where the modeling process and the prediction cycle for providing guidance run independently in parallel at every time can also be prevented.

[0107] Meanwhile, the electronic device (100) can periodically identify the frequency at which the aforementioned reference point change occurs for the subject (1). For example, if the modeling cycle is 2 weeks, the number of times the reference point change occurs every year can be monitored.

[0108] Here, the electronic device (100) may decrease the aforementioned constant value when the number of times a reference point change occurs is greater than or equal to the first number, and conversely, may increase the aforementioned constant value when the number of times is less than the second number (< first number). As a result, in addition to preventing a situation where a reference point change occurs too frequently, a situation where a prediction process is continuously performed in parallel due to the exclusion of a reference point change can also be prevented.

[0109] Meanwhile, the various embodiments described above may be implemented by combining two or more embodiments, provided that they do not conflict or contradict each other.

[0110] Meanwhile, the various embodiments described above may be implemented in a recording medium readable by a computer or a similar device using software, hardware, or a combination thereof.

[0111] According to hardware implementation, the embodiments described in this disclosure may be implemented using at least one of ASICs (Application Specific Integrated Circuits), DSPs (digital signal processors), DSPDs (digital signal processing devices), PLDs (programmable logic devices), FPGAs (field programmable gate arrays), processors, controllers, microcontrollers, microprocessors, and other electrical units for performing functions.

[0112] In some cases, the embodiments described herein may be implemented as the processor itself. In a software implementation, embodiments such as the procedures and functions described herein may be implemented as separate software modules. Each of the aforementioned software modules may perform one or more functions and operations described herein.

[0113] Meanwhile, computer instructions or computer programs for performing processing operations in electronic devices, etc., according to the various embodiments of the present disclosure described above may be stored in a non-transitory computer-readable medium. When such computer instructions or computer programs stored in the non-transitory computer-readable medium are executed by a processor of a specific device, the specific device described above performs processing operations in electronic devices, etc., according to the various embodiments described above.

[0114] A non-transient computer-readable medium refers to a medium that stores data semi-permanently and can be read by a device, unlike media that store data for a short period of time such as registers, caches, and memory. Specific examples of non-transient computer-readable media include CDs, DVDs, hard disks, Blu-ray discs, USBs, memory cards, and ROMs.

[0115] Although preferred embodiments of the present disclosure have been illustrated and described above, the present disclosure is not limited to the specific embodiments described above. It is understood that various modifications can be made by those skilled in the art without departing from the essence of the present disclosure as claimed in the claims, and such modifications should not be understood individually from the technical spirit or perspective of the present disclosure. Explanation of the symbols

[0116] 100: Electronic device 110: Memory 120: Communication interface 130: Processor 200: Terminal

Claims

Claim 1 A method of operating an electronic device comprises: a step of generating a digital twin to mimic the growth stage of a subject based on the subject's genetic data, biological data, and environmental data; a step of monitoring the growth of the digital twin to identify whether the growth state of the digital twin matches a preset stage or a preset state; and a step of providing guide information to the guardian's terminal of the subject when the growth state of the digital twin matches the preset state or the preset stage; wherein the step of providing guide information includes: a step of providing at least one query matching the preset stage or the preset state to the guardian's terminal; a step of identifying the subject's growth state information according to the response when a response to the query is received from the guardian's terminal; and a step of inputting the growth state information into a prediction model for predicting changes in the digital twin to obtain growth prediction information of the digital twin. and providing detailed guide information including at least one of a customized diet, medication guidance, exercise prescription, and growth prescription based on the growth prediction information; and the method of operation of the electronic device comprises: a step of training the prediction model based on the subject’s biometric data and environmental data input through the guardian’s terminal at multiple points in time; and a step of modeling the growth and development of the digital twin according to the age of the subject according to the modeling cycle and the sequential prediction results of the prediction model.The step of modeling the growth and development of the digital twin according to the age of the subject includes identifying a second time point, which is the next modeling time point, and a third time point, which is the modeling time point following the second time point, based on the current first time point; predicting the digital twin of the second time point based on the digital twin of the first time point; predicting the digital twin of the third time point based on the predicted digital twin of the second time point; if it is identified that the growth state of the digital twin of the predicted third time point exceeds a preset state or preset stage for the third time point, predicting a target time point in which the digital twin reaches a preset state or preset stage for the third time point; if the time interval between the second time point and the target time point is greater than or equal to a certain value, providing the guide information to the guardian's terminal at the target time point and modeling the digital twin by applying the modeling cycle based on the target time point; and if the time interval between the second time point and the target time point is less than the certain value, providing the guide information at the second time point and applying the modeling cycle based on the second time point to the digital twin The method of operation of an electronic device being modeled.; Claim 2 delete Claim 3 delete Claim 4 A method of operation of an electronic device according to claim 1, wherein the step of providing detailed guide information comprises comparing a growth goal set through the guardian's terminal with the growth prediction information through a generative model and providing detailed guide information including at least one of a customized diet, medication guidance, exercise prescription, and growth prescription. Claim 5 A method of operation of an electronic device according to claim 4, wherein the step of providing detailed guide information comprises selecting at least one guide item among a customized diet, medication guidance, exercise prescription, and growth prescription according to the output of the generative model, selecting at least one product suitable for the subject and the guardian through the generative model from a product list containing a plurality of products related to the selected guide item, and providing information about the selected product to the guardian's terminal. Claim 6 An electronic device comprising: a memory storing at least one instruction; and a processor that executes the instruction to perform the method of operation of claim 1.

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